Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules

• URL: http://arxiv.org/abs/2409.16238v2
• Date: Fri, 28 Feb 2025 16:29:51 GMT
• Title: Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules
• Authors: Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura,
• Abstract summary: We introduce precision and recall for logical rules and define their composition as rule utility.<n>We also introduce SPECTRUM, a scalable framework for learning logical theories from relational data.
• Score: 9.303501974597548
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Probabilistic logical models are a core component of neurosymbolic AI and are important in their own right for tasks that require high explainability. Unlike neural networks, logical theories that underlie the model are often handcrafted using domain expertise, making their development costly and prone to errors. While there are algorithms that learn logical theories from data, they are generally prohibitively expensive, limiting their applicability in real-world settings. Here, we introduce precision and recall for logical rules and define their composition as rule utility -- a cost-effective measure of the predictive power of logical theories. We also introduce SPECTRUM, a scalable framework for learning logical theories from relational data. Its scalability derives from a linear-time algorithm that mines recurrent subgraphs in the data graph along with a second algorithm that, using the cheap utility measure, efficiently ranks rules derived from these subgraphs. Finally, we prove theoretical guarantees on the utility of the learnt logical theory. As a result, we demonstrate across various tasks that SPECTRUM scales to larger datasets, often learning more accurate logical theories on CPUs in < 1% the runtime of SOTA neural network approaches on GPUs.

Related papers

• Extracting Interpretable Logic Rules from Graph Neural Networks [7.262955921646326]
  Graph neural networks (GNNs) operate over both input feature spaces and graph structures. We propose a novel framework, LOGI CXGNN, for extracting interpretable logic rules from GNNs. LOGI CXGNN is model-agnostic, efficient, and data-driven, eliminating the need for predefined concepts.
  arXiv  Detail & Related papers  (2025-03-25T09:09:46Z)
• Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
  We propose a complex reasoning schema over KG upon large language models (LLMs) We augment the arbitrary first-order logical queries via binary tree decomposition to stimulate the reasoning capability of LLMs. Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods.
  arXiv  Detail & Related papers  (2024-05-02T18:12:08Z)
• On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method [59.52204415829695]
  Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications. This paper investigates the generalization ability of different TGL algorithms. We propose a simplified TGL network, which enjoys a small generalization error, improved overall performance, and lower model complexity.
  arXiv  Detail & Related papers  (2024-02-26T08:22:22Z)
• Learning Interpretable Rules for Scalable Data Representation and Classification [11.393431987232425]
  Rule-based Learner Representation (RRL) learns interpretable non-fuzzy rules for data representation and classification. RRL can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios.
  arXiv  Detail & Related papers  (2023-10-22T15:55:58Z)
• Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models [5.317624228510748]
  Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. We present the first principled approach for mining structural motifs in lifted graphical models. We show that we outperform state-of-the-art structure learning approaches by up to 6% in terms of accuracy and up to 80% in terms of runtime.
  arXiv  Detail & Related papers  (2023-02-09T12:21:55Z)
• Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [65.23508422635862]
  We propose learning rules with the recently proposed logical neural networks (LNN) Compared to others, LNNs offer strong connection to classical Boolean logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable.
  arXiv  Detail & Related papers  (2021-12-06T19:38:30Z)
• Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification [69.26747803963907]
  Rank-R Feedforward Neural Network (FNN) is a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. We establish the universal approximation and learnability properties of Rank-R FNN, and we validate its performance on real-world hyperspectral datasets.
  arXiv  Detail & Related papers  (2021-04-11T16:37:32Z)
• Embedding Symbolic Temporal Knowledge into Deep Sequential Models [21.45383857094518]
  Sequences and time-series often arise in robot tasks, e.g., in activity recognition and imitation learning. Deep neural networks (DNNs) have emerged as an effective data-driven methodology for processing sequences given sufficient training data and compute resources. We construct semantic-based embeddings of automata generated from formula via a Graph Neural Network. Experiments show that these learnt embeddings can lead to improvements in downstream robot tasks such as sequential action recognition and imitation learning.
  arXiv  Detail & Related papers  (2021-01-28T13:17:46Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
  This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks. Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
  arXiv  Detail & Related papers  (2020-10-08T14:47:02Z)
• Neural Logic Reasoning [47.622957656745356]
  We propose Logic-Integrated Neural Network (LINN) to integrate the power of deep learning and logic reasoning. LINN learns basic logical operations such as AND, OR, NOT as neural modules, and conducts propositional logical reasoning through the network for inference. Experiments show that LINN significantly outperforms state-of-the-art recommendation models in Top-K recommendation.
  arXiv  Detail & Related papers  (2020-08-20T14:53:23Z)
• Learning Reasoning Strategies in End-to-End Differentiable Proving [50.9791149533921]
  Conditional Theorem Provers learn optimal rule selection strategy via gradient-based optimisation. We show that Conditional Theorem Provers are scalable and yield state-of-the-art results on the CLUTRR dataset.
  arXiv  Detail & Related papers  (2020-07-13T16:22:14Z)
• Evaluating Logical Generalization in Graph Neural Networks [59.70452462833374]
  We study the task of logical generalization using graph neural networks (GNNs) Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics. We find that the ability for models to generalize and adapt is strongly determined by the diversity of the logical rules they encounter during training.
  arXiv  Detail & Related papers  (2020-03-14T05:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.